Abstract
A self-organizing fuzzy system (SOFS) is presented. A plant model is not required for training, that is, the plant model is unknown to the SOFS. Using new data types, the vectors and matrices, a concise formulation is developed for the organization process and the inference activities of the SOFS. The fuzzy system can learn its rule-based structure and parameters from input/output training data. There is no fuzzy IF-THEN rule in the system initially. The fuzzy control policy is constructed automatically during learning process when the system is simulated by input/output training data. With the well-known random optimization (RO) method, the generated fuzzy system can learn its parameters for specific applications. The proposed SOFS is applied on temperature control problem.
| Original language | English |
|---|---|
| Pages (from-to) | 473-477 |
| Number of pages | 5 |
| Journal | Conference Record - IAS Annual Meeting (IEEE Industry Applications Society) |
| Volume | 1 |
| State | Published - 2002 |
| Event | 37th IAS Annual Meeting and World Conference on Industrial applications of Electrical Energy - Pittsburgh, PA, United States Duration: 13 10 2002 → 18 10 2002 |
Keywords
- Clustering
- Fuzzy control
- Inverse learning control
- Random optimization
- Self-learning
- Self-organization
- Temperature control